Summary

This chapter served as an in-depth exploration into fundamental feature engineering methodologies, complemented by practical demonstrations within Jupyter notebooks using the house prices dataset sourced from Kaggle. Through a systematic approach, you practiced using various feature engineering techniques that are useful for performing predictive modeling. You learned about handling missing values, transforming numeric data, and how to handle skewed data. You also performed feature engineering on temporal data, creating new columns that enable insights into the evolution of housing attributes over time.

A pivotal lesson that was underscored throughout this chapter is the importance of splitting data at the outset of your analysis. By segregating your data into distinct training and testing sets before feature engineering, you safeguard against data leakage and uphold the integrity of your model evaluation process. This foundational step lays the groundwork for robust model...